A Single Machine Scheduling Problem with Individual Job Tardiness based Objectives

377-382A multi-objective scheduling problem with specified release times and due dates for individual tasks is analysed in this study. Distinct tardiness value of each task j comprises the part of the objective, while it is desired to identify all non-dominated solutions. Tardiness values for a total number of n tasks complete a single solution making it an n-objective scheduling problem. Tardiness is treated here as a task specific objective, being different in the usual scheduling context. A branch and bound procedure is proposed for individual tardiness of tasks in multi-objective contexts. The procedure is illustrated with an example. Active schedule enumeration scheme with depth-first strategy for branching is used in branching while two different bounding schemes are tested. However, an improved bounding scheme to find better-quality need to be developed. Procedure is found to perform well on small scale problems. For an n-objective problem like this, a more robust data structure may further improve the performance of the procedure

A multidisciplinary approach to triage patients with breast disease during the COVID-19 pandemic: Experience from a tertiary care center in the developing world

Background: The COVID-19 pandemic has created a need to prioritize care because of limitation of resources. Owing to the heterogeneity and high prevalence of breast cancers, the need to prioritize care in this vulnerable population is essential. While various medical societies have published recommendations to manage breast disease during the COVID-19 pandemic, most are focused on the Western world and do not necessarily address the challenges of a resource-limited setting.Aim: In this article, we describe our institutional approach for prioritizing care for patients presenting with breast disease.Methods and results: The breast disease management guidelines were developed and approved with the expertise of the Multidisciplinary Breast Program Leadership Committee (BPLC) of the Aga Khan University, Karachi, Pakistan. These guidelines were inspired, adapted, and modified keeping in view the needs of our resource-limited healthcare system. These recommendations are also congruent with the ethical guidelines developed by the Center of Biomedical Ethics and Culture (CBEC) at the Sindh Institute of Urology and Transplantation (SIUT), Karachi. Our institutional recommendations outline a framework to triage patients based on the urgency of care, scheduling conflicts, and tumor board recommendations, optimizing healthcare workers\u27 schedules, operating room reallocation, and protocols. We also describe the Virtual Blended Clinics , a resource-friendly means of conducting virtual clinics and a comprehensive plan for transitioning back into the post-COVID routine.Conclusion: Our institutional experience may be considered as a guide during the COVID-19 pandemic, particularly for triaging care in a resource-limited setting; however, these are not meant to be universally applicable, and individual cases must be tailored based on physicians\u27 clinical judgment to provide the best quality care

Formulation and structural insight of biocompatible microemulsion for enhanced release profile of anticancer methotrexate

Microemulsions (μEs) are particularly suitable systems for the efficient delivery of anticancer drugs due to their thermodynamic stability, structural flexibility, and patient-friendly chemotherapies. Moreover, μE formulations can efficiently encapsulate the anticancer drugs and deliver them to the desired location. Herein, three new Tween-60-based µE formulations were developed to enhance the dissolution profile of anticancer methotrexate (MTX). For this, μE formulations using an appropriate ratio of castor oil (∼9%), water (∼11%), and Tween-60 (∼40%) were used, while ethanol, 2-propanol, and 1-butanol were selected as co-surfactants for each formulation, respectively. Preliminarily, the phase compatibility of the μE ingredients, the average μE region, and the structural transformation in the microstructure of μE were delineated by mapping the pseudoternary phase diagram, as well as electrical conductivity, viscosity, and optical microscopic measurements. The size distribution profile of the as-formulated μEs analyzed by dynamic light scattering (DLS) revealed the fine monomodal assembly of MTX-μE nanodroplets (∼65 nm), which remained stable over a half year of storage. FTIR analysis showed good compatibility of MTX with μE ingredients with no apparent chemical interaction, while fluorescence measurements endorsed the acquisition of MTX in nonpolar microenvironments. Furthermore, an enhanced dissolution rate (>98% ± 1.5%, p ≤ 0.001) and superior bioavailability of the lyophilized non-aggregated methotrexate nanoparticles (MTX-NPs) were achieved, making them a suitable formulation for oral administration

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

‘Recklessness, hubris, & greed’: defining accountability for directors of large private companies

Accountability has been seen as a panacea, for curbing the unfettered power of individuals, with appropriate checks and balances. It has become mainly synonymous with regulating the behaviour of directors and the corporate governance of companies. Whilst there has been copious research regarding the accountability of directors in public limited companies, the accountability of directors of large private companies (LPCs) has not been explored. Although, when a corporate scandal/collapse has occurred; there is an acceptance that the accountability of directors was absent. However, there is little agreement on defining, developing and maintaining the modern concept of accountability. This research bridges this gap, by answering the central question of how directors of large private companies (LPCs) in the UK should be held accountable for their actions and decisions. As a result, it proposes a new theory of accountability termed ‘Holistic Accountability’ tailored to directors of LPCs. The generalisation of accountability definitions makes way for ‘Holistic Accountability’ due to the unique nature of LPCs. Indeed, some of the reasons why the accountability of directors of LPCs is needed are evidenced by three case studies which show that directors of LPCs can freely engage in illegal/questionable conduct. In considering which constituent is best placed to hold directors of LPCs accountable, the government stands out as the only possible constituent. Therefore, the current regulatory framework is examined to determine whether it enables the holistic accountability of directors of LPCs through the Financial Reporting Council (FRC) and the Pensions Regulator (TPR). The research finds that the focus of the FRC and TPR on financial reporting and pension schemes respectively fails to adequately fulfil each of the holistic accountability elements. In this regard, the roles of the Securities & Exchange Commission (SEC) and the Australian Securities & Investments Commission (ASIC), are also considered to determine what lessons could be learned in respect of the holistic accountability of directors in LPCs in the UK. The gaps identified in holding directors of LPCs holistically accountable through the current framework are also addressed, by building on some of the effective measures taken by both the SEC and ASIC for holistic accountability. For each of the proposals, recommendations for future research are put forward. Accordingly, the thesis concludes a UK corporate regulator should be established to enforce the holistic accountability of directors of LPCs. Failure to do so would mean the exacerbation of the current ‘black-box’ issue and further corporate collapses and scandals as a result of director misconduct

Development of palliative care services at a tertiary care teaching hospital in Pakistan: Retrospective analysis of existing palliative care program

Context: Palliative care (PC) is an important aspect of providing holistic care to patients and their families who are dealing with a serious or life limiting illness. Medical community and public poorly understand the implications and benefits of these services. Unfortunately, because of this, PC remains a neglected area of healthcare in the most institutions of Pakistan. Objectives: We sought to review the current structure, barriers in context of growing need for PC, possible means to overcome these challenges and future perspectives at tertiary care hospital. Methods: Retrospective longitudinal cross-sectional study was done using data from 2017 to 2019 in the section of PC at Aga Khan University Hospital (AKUH). Results: PC program has been self-sustainable and serving 3747 patients in 2017-2019. The results show that palliative care services (PCS) are well integrated for oncology with all three models of PCS delivery. Most of the patients opted for comfort code during hospital stay and preferred end-of-life-care at home. We received less referral from outside the hospital and other specialties but received more self-referrals surprisingly. Home-based-palliative-care was also a key aspect of the program. PCS providing quality of care and nearly reaching target goal of quality indicators. Conclusion: The enormous burden of life-threatening illnesses is associated with physical and psychosocial sufferings, which explains the illustrious need for PC in developing countries such as Pakistan. PCS at AKUH initiated in 2017. Nevertheless, there are challenges to service expansion and progress, which are being addressed

Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing

Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing

Evaluation of postgraduate family medicine trainee\u27s knowledge and attitude following an online educational module in palliative care: A descriptive study

Objectives: To identify improvement in knowledge and attitude of Family Medicine (FM) postgraduate trainees (PGT) towards Palliative care (PC) in order to provide effective care to the patients with advanced disease.Methods: A cross-sectional study was conducted over eight weeks from 1st July till 3rd September 2021 at Family Medicine Department, Aga Khan University Hospital (AKUH). PGT who willingly signed the written informed consent were enrolled in the study. Descriptive analysis, frequencies, proportions and thematic approach were used for data analysis. Data was analyzed using SPSS version 23.Results: FM-PGT were included in the study. Improvement in knowledge was observed in posttest scores along with positive change in their attitude and improved perception of level of confidence for managing PC patients. Overall assessment of PCM was positive.Conclusion: This PCM seems to be a useful tool for PC training in postgraduate medical education (PGME). This highlights some useful aspects for future applications in PC education and training

Performance Prediction for Undergraduate Degree Programs Using Machine Learning Techniques - A Preliminary Review

Academic Performance prediction for undergraduate students is considered as one of the hot research areas since last couple of decades. An accurate and timely prediction of the student’s performance can directly influence the three participants; learner, instructor and the institution. This study presents a brief, preliminary review to explore existing literature from 2010 to 2022 in the context of performance prediction for Undergraduate Degree Programs (UDP). This review is organized according to Online and Traditional Education Systems (TES), and granularity level of performance output i.e., Degree program (Final CGPA), Next-semester, and the Course level grades. Aggregate analysis of the extracted data reveals that course level prediction is highly worked area deploying classification and regression techniques using data from academic domain. Existing empirical studies are mostly evaluated using accuracy, precision, recall and F1-measure and are validated with 10-fold cross validation. Contribution of this study is the novel categorical distribution of studies with respect to education system and granularity levels. Another important finding was the Success ratio of different Machine learning (ML) techniques used for these prediction studies. It is concluded that further research is required for TES to discover interdependent group of courses and Course Clusters for a certain degree program and then to develop prediction models for those course clusters